Overview

Dataset statistics

Number of variables36
Number of observations301761
Missing cells2097739
Missing cells (%)19.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory744.9 MiB
Average record size in memory2.5 KiB

Variable types

Categorical23
Text7
Numeric5
Unsupported1

Alerts

RI has constant value ""Constant
Accident_Classification is highly imbalanced (80.6%)Imbalance
Main_Cause is highly imbalanced (66.5%)Imbalance
Surface_Type is highly imbalanced (61.0%)Imbalance
Surface_Condition is highly imbalanced (56.2%)Imbalance
Road_Condition is highly imbalanced (56.0%)Imbalance
Spot_Conditions is highly imbalanced (80.0%)Imbalance
Accident_SpotB has 125077 (41.4%) missing valuesMissing
Lane_Type has 239253 (79.3%) missing valuesMissing
Road_Markings has 271601 (90.0%) missing valuesMissing
Spot_Conditions has 248412 (82.3%) missing valuesMissing
Side_Walk has 254704 (84.4%) missing valuesMissing
RoadJunction has 301761 (100.0%) missing valuesMissing
Collision_TypeB has 125075 (41.4%) missing valuesMissing
landmark_second has 255792 (84.8%) missing valuesMissing
Distance_LandMark_Second has 274895 (91.1%) missing valuesMissing
Crime_No has unique valuesUnique
RoadJunction is an unsupported type, check if it needs cleaning or further analysisUnsupported
Latitude has 204549 (67.8%) zerosZeros
Longitude has 204552 (67.8%) zerosZeros

Reproduction

Analysis started2024-04-14 05:24:58.086517
Analysis finished2024-04-14 05:27:17.430735
Duration2 minutes and 19.34 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DISTRICTNAME
Categorical

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 MiB
Bengaluru City
34526 
Tumakuru
 
16738
Bengaluru Dist
 
15436
Hassan
 
15365
Mandya
 
15295
Other values (33)
204401 

Length

Max length21
Median length16
Mean length10.372083
Min length5

Characters and Unicode

Total characters3129890
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBagalkot
2nd rowBagalkot
3rd rowBagalkot
4th rowBagalkot
5th rowBagalkot

Common Values

ValueCountFrequency (%)
Bengaluru City 34526
 
11.4%
Tumakuru 16738
 
5.5%
Bengaluru Dist 15436
 
5.1%
Hassan 15365
 
5.1%
Mandya 15295
 
5.1%
Belagavi Dist 15058
 
5.0%
Chitradurga 11899
 
3.9%
Shivamogga 11583
 
3.8%
Mysuru Dist 10930
 
3.6%
Ramanagara 10711
 
3.5%
Other values (28) 144220
47.8%

Length

2024-04-14T10:57:17.561125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 60153
 
14.2%
bengaluru 49962
 
11.8%
dist 41424
 
9.8%
belagavi 19663
 
4.7%
mysuru 17092
 
4.0%
tumakuru 16738
 
4.0%
kannada 15389
 
3.6%
hassan 15365
 
3.6%
mandya 15295
 
3.6%
chitradurga 11899
 
2.8%
Other values (28) 159291
37.7%

Most occurring characters

ValueCountFrequency (%)
a 587838
18.8%
u 268700
 
8.6%
i 223047
 
7.1%
r 221684
 
7.1%
g 171168
 
5.5%
n 152960
 
4.9%
t 137063
 
4.4%
l 134716
 
4.3%
120510
 
3.9%
y 104146
 
3.3%
Other values (28) 1008058
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3129890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 587838
18.8%
u 268700
 
8.6%
i 223047
 
7.1%
r 221684
 
7.1%
g 171168
 
5.5%
n 152960
 
4.9%
t 137063
 
4.4%
l 134716
 
4.3%
120510
 
3.9%
y 104146
 
3.3%
Other values (28) 1008058
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3129890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 587838
18.8%
u 268700
 
8.6%
i 223047
 
7.1%
r 221684
 
7.1%
g 171168
 
5.5%
n 152960
 
4.9%
t 137063
 
4.4%
l 134716
 
4.3%
120510
 
3.9%
y 104146
 
3.3%
Other values (28) 1008058
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3129890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 587838
18.8%
u 268700
 
8.6%
i 223047
 
7.1%
r 221684
 
7.1%
g 171168
 
5.5%
n 152960
 
4.9%
t 137063
 
4.4%
l 134716
 
4.3%
120510
 
3.9%
y 104146
 
3.3%
Other values (28) 1008058
32.2%
Distinct727
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.9 MiB
2024-04-14T10:57:18.014094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length44
Median length23
Mean length15.777781
Min length6

Characters and Unicode

Total characters4761119
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowAmengad PS
2nd rowAmengad PS
3rd rowAmengad PS
4th rowAmengad PS
5th rowAmengad PS
ValueCountFrequency (%)
ps 297892
37.8%
traffic 95936
 
12.2%
rural 36020
 
4.6%
town 11634
 
1.5%
south 5614
 
0.7%
north 4807
 
0.6%
nelamangala 4333
 
0.5%
v 4320
 
0.5%
station 3870
 
0.5%
police 3869
 
0.5%
Other values (654) 320586
40.6%
2024-04-14T10:57:18.541074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 729915
15.3%
492706
 
10.3%
r 341326
 
7.2%
S 337405
 
7.1%
P 315279
 
6.6%
i 267336
 
5.6%
l 196834
 
4.1%
f 193542
 
4.1%
u 185963
 
3.9%
n 164082
 
3.4%
Other values (43) 1536731
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4761119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 729915
15.3%
492706
 
10.3%
r 341326
 
7.2%
S 337405
 
7.1%
P 315279
 
6.6%
i 267336
 
5.6%
l 196834
 
4.1%
f 193542
 
4.1%
u 185963
 
3.9%
n 164082
 
3.4%
Other values (43) 1536731
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4761119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 729915
15.3%
492706
 
10.3%
r 341326
 
7.2%
S 337405
 
7.1%
P 315279
 
6.6%
i 267336
 
5.6%
l 196834
 
4.1%
f 193542
 
4.1%
u 185963
 
3.9%
n 164082
 
3.4%
Other values (43) 1536731
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4761119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 729915
15.3%
492706
 
10.3%
r 341326
 
7.2%
S 337405
 
7.1%
P 315279
 
6.6%
i 267336
 
5.6%
l 196834
 
4.1%
f 193542
 
4.1%
u 185963
 
3.9%
n 164082
 
3.4%
Other values (43) 1536731
32.3%

Crime_No
Real number (ℝ)

UNIQUE 

Distinct301761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0532327 × 1016
Minimum1.0438011 × 1016
Maximum1.0986189 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-04-14T10:57:18.813030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.0438011 × 1016
5-th percentile1.0440156 × 1016
Q11.0444147 × 1016
median1.0455217 × 1016
Q31.0464164 × 1016
95-th percentile1.0980106 × 1016
Maximum1.0986189 × 1016
Range5.481779 × 1014
Interquartile range (IQR)2.0017 × 1013

Descriptive statistics

Standard deviation1.8842034 × 1014
Coefficient of variation (CV)0.017889716
Kurtosis1.7586905
Mean1.0532327 × 1016
Median Absolute Deviation (MAD)1.10034 × 1013
Skewness1.9343989
Sum5.4055066 × 1018
Variance3.5502225 × 1028
MonotonicityNot monotonic
2024-04-14T10:57:19.061471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.047012452 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
1.098021712 × 10161
 
< 0.1%
Other values (301751) 301751
> 99.9%
ValueCountFrequency (%)
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
1.043801062 × 10161
< 0.1%
ValueCountFrequency (%)
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%
1.098618852 × 10161
< 0.1%

Year
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4551
Minimum2016
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-04-14T10:57:19.265145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32022
95-th percentile2023
Maximum2023
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.3251807
Coefficient of variation (CV)0.0011513902
Kurtosis-1.2714899
Mean2019.4551
Median Absolute Deviation (MAD)2
Skewness0.052741483
Sum6.0939278 × 108
Variance5.4064653
MonotonicityNot monotonic
2024-04-14T10:57:19.459902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2018 39971
13.2%
2023 39635
13.1%
2016 39271
13.0%
2019 39228
13.0%
2017 38976
12.9%
2022 38140
12.6%
2021 33557
11.1%
2020 32983
10.9%
ValueCountFrequency (%)
2016 39271
13.0%
2017 38976
12.9%
2018 39971
13.2%
2019 39228
13.0%
2020 32983
10.9%
2021 33557
11.1%
2022 38140
12.6%
2023 39635
13.1%
ValueCountFrequency (%)
2023 39635
13.1%
2022 38140
12.6%
2021 33557
11.1%
2020 32983
10.9%
2019 39228
13.0%
2018 39971
13.2%
2017 38976
12.9%
2016 39271
13.0%

RI
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
1
301761 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301761
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 301761
100.0%

Length

2024-04-14T10:57:19.657255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:19.825826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 301761
100.0%

Most occurring characters

ValueCountFrequency (%)
1 301761
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 301761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 301761
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 301761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 301761
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 301761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 301761
100.0%

Noofvehicle_involved
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.578819
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-04-14T10:57:19.984263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum75
Range74
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.61518965
Coefficient of variation (CV)0.38965179
Kurtosis1470.954
Mean1.578819
Median Absolute Deviation (MAD)0
Skewness15.9525
Sum476426
Variance0.37845831
MonotonicityNot monotonic
2024-04-14T10:57:20.176943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 160097
53.1%
1 135274
44.8%
3 5496
 
1.8%
4 629
 
0.2%
5 150
 
< 0.1%
6 56
 
< 0.1%
7 23
 
< 0.1%
8 11
 
< 0.1%
22 6
 
< 0.1%
9 4
 
< 0.1%
Other values (11) 15
 
< 0.1%
ValueCountFrequency (%)
1 135274
44.8%
2 160097
53.1%
3 5496
 
1.8%
4 629
 
0.2%
5 150
 
< 0.1%
6 56
 
< 0.1%
7 23
 
< 0.1%
8 11
 
< 0.1%
9 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
75 1
 
< 0.1%
65 1
 
< 0.1%
52 1
 
< 0.1%
49 1
 
< 0.1%
46 1
 
< 0.1%
36 1
 
< 0.1%
22 6
< 0.1%
21 2
 
< 0.1%
20 1
 
< 0.1%
12 2
 
< 0.1%

Accident_Classification
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size20.5 MiB
Road Accidents
278101 
Not Applicable
 
20090
Rail Road Accidents
 
3057
Other Railway Accidents
 
510
KURUB
 
1

Length

Max length23
Median length14
Mean length14.065834
Min length5

Characters and Unicode

Total characters4244492
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRoad Accidents
2nd rowRoad Accidents
3rd rowRoad Accidents
4th rowRoad Accidents
5th rowRoad Accidents

Common Values

ValueCountFrequency (%)
Road Accidents 278101
92.2%
Not Applicable 20090
 
6.7%
Rail Road Accidents 3057
 
1.0%
Other Railway Accidents 510
 
0.2%
KURUB 1
 
< 0.1%
(Missing) 2
 
< 0.1%

Length

2024-04-14T10:57:20.396080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:20.569560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
accidents 281668
46.4%
road 281158
46.3%
not 20090
 
3.3%
applicable 20090
 
3.3%
rail 3057
 
0.5%
other 510
 
0.1%
railway 510
 
0.1%
kurub 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 583426
13.7%
d 562826
13.3%
a 305325
7.2%
305325
7.2%
i 305325
7.2%
e 302268
7.1%
t 302268
7.1%
A 301758
7.1%
o 301248
7.1%
R 284726
6.7%
Other values (14) 689997
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4244492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 583426
13.7%
d 562826
13.3%
a 305325
7.2%
305325
7.2%
i 305325
7.2%
e 302268
7.1%
t 302268
7.1%
A 301758
7.1%
o 301248
7.1%
R 284726
6.7%
Other values (14) 689997
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4244492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 583426
13.7%
d 562826
13.3%
a 305325
7.2%
305325
7.2%
i 305325
7.2%
e 302268
7.1%
t 302268
7.1%
A 301758
7.1%
o 301248
7.1%
R 284726
6.7%
Other values (14) 689997
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4244492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 583426
13.7%
d 562826
13.3%
a 305325
7.2%
305325
7.2%
i 305325
7.2%
e 302268
7.1%
t 302268
7.1%
A 301758
7.1%
o 301248
7.1%
R 284726
6.7%
Other values (14) 689997
16.3%

Accident_Spot
Categorical

Distinct20
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size19.3 MiB
Not Applicable
78483 
Narrow road
65991 
Other
56061 
Cross roads
27170 
Curves
25983 
Other values (15)
48072 

Length

Max length26
Median length22
Mean length9.9389515
Min length5

Characters and Unicode

Total characters2999178
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBottleneck
2nd rowBridge
3rd rowBottleneck
4th rowBottleneck
5th rowCross roads

Common Values

ValueCountFrequency (%)
Not Applicable 78483
26.0%
Narrow road 65991
21.9%
Other 56061
18.6%
Cross roads 27170
 
9.0%
Curves 25983
 
8.6%
Junction 16402
 
5.4%
Circle 13336
 
4.4%
Road hump or Rumble strips 3712
 
1.2%
Offset 3482
 
1.2%
Bridge 3177
 
1.1%
Other values (10) 7963
 
2.6%

Length

2024-04-14T10:57:20.774606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
not 78483
15.9%
applicable 78483
15.9%
road 69703
14.1%
narrow 65991
13.3%
other 56061
11.3%
cross 27170
 
5.5%
roads 27170
 
5.5%
curves 25983
 
5.3%
junction 19315
 
3.9%
circle 13678
 
2.8%
Other values (22) 32461
6.6%

Most occurring characters

ValueCountFrequency (%)
r 361842
 
12.1%
o 297090
 
9.9%
a 243949
 
8.1%
192738
 
6.4%
e 192342
 
6.4%
l 178577
 
6.0%
t 168703
 
5.6%
p 164390
 
5.5%
N 144474
 
4.8%
s 120002
 
4.0%
Other values (26) 935071
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2999178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 361842
 
12.1%
o 297090
 
9.9%
a 243949
 
8.1%
192738
 
6.4%
e 192342
 
6.4%
l 178577
 
6.0%
t 168703
 
5.6%
p 164390
 
5.5%
N 144474
 
4.8%
s 120002
 
4.0%
Other values (26) 935071
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2999178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 361842
 
12.1%
o 297090
 
9.9%
a 243949
 
8.1%
192738
 
6.4%
e 192342
 
6.4%
l 178577
 
6.0%
t 168703
 
5.6%
p 164390
 
5.5%
N 144474
 
4.8%
s 120002
 
4.0%
Other values (26) 935071
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2999178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 361842
 
12.1%
o 297090
 
9.9%
a 243949
 
8.1%
192738
 
6.4%
e 192342
 
6.4%
l 178577
 
6.0%
t 168703
 
5.6%
p 164390
 
5.5%
N 144474
 
4.8%
s 120002
 
4.0%
Other values (26) 935071
31.2%
Distinct4
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size19.5 MiB
Rural Areas
171887 
City/Town
111941 
Villages settlement
 
11279
Not Applicable
 
6651

Length

Max length19
Median length11
Mean length10.623218
Min length9

Characters and Unicode

Total characters3205641
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRural Areas
2nd rowVillages settlement
3rd rowCity/Town
4th rowRural Areas
5th rowCity/Town

Common Values

ValueCountFrequency (%)
Rural Areas 171887
57.0%
City/Town 111941
37.1%
Villages settlement 11279
 
3.7%
Not Applicable 6651
 
2.2%
(Missing) 3
 
< 0.1%

Length

2024-04-14T10:57:20.952308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:21.122762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
rural 171887
35.0%
areas 171887
35.0%
city/town 111941
22.8%
villages 11279
 
2.3%
settlement 11279
 
2.3%
not 6651
 
1.4%
applicable 6651
 
1.4%

Most occurring characters

ValueCountFrequency (%)
a 361704
 
11.3%
r 343774
 
10.7%
e 223654
 
7.0%
l 219026
 
6.8%
s 194445
 
6.1%
189817
 
5.9%
A 178538
 
5.6%
R 171887
 
5.4%
u 171887
 
5.4%
t 152429
 
4.8%
Other values (15) 998480
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3205641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 361704
 
11.3%
r 343774
 
10.7%
e 223654
 
7.0%
l 219026
 
6.8%
s 194445
 
6.1%
189817
 
5.9%
A 178538
 
5.6%
R 171887
 
5.4%
u 171887
 
5.4%
t 152429
 
4.8%
Other values (15) 998480
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3205641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 361704
 
11.3%
r 343774
 
10.7%
e 223654
 
7.0%
l 219026
 
6.8%
s 194445
 
6.1%
189817
 
5.9%
A 178538
 
5.6%
R 171887
 
5.4%
u 171887
 
5.4%
t 152429
 
4.8%
Other values (15) 998480
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3205641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 361704
 
11.3%
r 343774
 
10.7%
e 223654
 
7.0%
l 219026
 
6.8%
s 194445
 
6.1%
189817
 
5.9%
A 178538
 
5.6%
R 171887
 
5.4%
u 171887
 
5.4%
t 152429
 
4.8%
Other values (15) 998480
31.1%
Distinct15
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size20.4 MiB
Open area
144878 
Near Bus stop
30213 
Residential area
30135 
Near or inside a village
25414 
Near School or College
 
12607
Other values (10)
58513 

Length

Max length30
Median length9
Mean length13.86925
Min length9

Characters and Unicode

Total characters4185185
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen area
2nd rowNarrow bridge or culverts
3rd rowNear School or College
4th rowResidential area
5th rowAt pedestrian Crossing

Common Values

ValueCountFrequency (%)
Open area 144878
48.0%
Near Bus stop 30213
 
10.0%
Residential area 30135
 
10.0%
Near or inside a village 25414
 
8.4%
Near School or College 12607
 
4.2%
Near office complex 10757
 
3.6%
Near Petrol Pump 10621
 
3.5%
Near a religious place 7528
 
2.5%
Near Hospital 6780
 
2.2%
In bazaar 6675
 
2.2%
Other values (5) 16152
 
5.4%

Length

2024-04-14T10:57:21.292067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 180862
22.4%
open 144878
18.0%
near 111114
13.8%
or 43439
 
5.4%
a 40136
 
5.0%
bus 30213
 
3.7%
stop 30213
 
3.7%
residential 30135
 
3.7%
inside 25414
 
3.2%
village 25414
 
3.2%
Other values (24) 144418
17.9%

Most occurring characters

ValueCountFrequency (%)
e 644829
15.4%
a 627547
15.0%
504476
12.1%
r 402791
9.6%
n 222721
 
5.3%
p 215074
 
5.1%
i 194815
 
4.7%
l 174610
 
4.2%
o 174068
 
4.2%
s 150994
 
3.6%
Other values (25) 873260
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4185185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 644829
15.4%
a 627547
15.0%
504476
12.1%
r 402791
9.6%
n 222721
 
5.3%
p 215074
 
5.1%
i 194815
 
4.7%
l 174610
 
4.2%
o 174068
 
4.2%
s 150994
 
3.6%
Other values (25) 873260
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4185185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 644829
15.4%
a 627547
15.0%
504476
12.1%
r 402791
9.6%
n 222721
 
5.3%
p 215074
 
5.1%
i 194815
 
4.7%
l 174610
 
4.2%
o 174068
 
4.2%
s 150994
 
3.6%
Other values (25) 873260
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4185185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 644829
15.4%
a 627547
15.0%
504476
12.1%
r 402791
9.6%
n 222721
 
5.3%
p 215074
 
5.1%
i 194815
 
4.7%
l 174610
 
4.2%
o 174068
 
4.2%
s 150994
 
3.6%
Other values (25) 873260
20.9%

Accident_SpotB
Categorical

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing125077
Missing (%)41.4%
Memory size18.1 MiB
Straight and flat
77684 
Other
70015 
Curve
22398 
Bridge
 
3273
Construction Work / Material
 
1027
Other values (3)
 
2287

Length

Max length28
Median length5
Mean length10.536195
Min length5

Characters and Unicode

Total characters1861577
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowCurve
3rd rowOther
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Straight and flat 77684
25.7%
Other 70015
23.2%
Curve 22398
 
7.4%
Bridge 3273
 
1.1%
Construction Work / Material 1027
 
0.3%
Pot holed 1023
 
0.3%
Steep Incline or Climb 829
 
0.3%
Culvert 435
 
0.1%
(Missing) 125077
41.4%

Length

2024-04-14T10:57:21.457441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:21.665127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
straight 77684
22.9%
and 77684
22.9%
flat 77684
22.9%
other 70015
20.7%
curve 22398
 
6.6%
bridge 3273
 
1.0%
material 1027
 
0.3%
1027
 
0.3%
work 1027
 
0.3%
construction 1027
 
0.3%
Other values (7) 5797
 
1.7%

Most occurring characters

ValueCountFrequency (%)
t 308435
16.6%
a 235106
12.6%
r 177715
9.5%
161959
8.7%
h 148722
 
8.0%
e 100658
 
5.4%
i 84669
 
4.5%
d 81980
 
4.4%
l 81827
 
4.4%
n 81396
 
4.4%
Other values (20) 399110
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1861577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 308435
16.6%
a 235106
12.6%
r 177715
9.5%
161959
8.7%
h 148722
 
8.0%
e 100658
 
5.4%
i 84669
 
4.5%
d 81980
 
4.4%
l 81827
 
4.4%
n 81396
 
4.4%
Other values (20) 399110
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1861577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 308435
16.6%
a 235106
12.6%
r 177715
9.5%
161959
8.7%
h 148722
 
8.0%
e 100658
 
5.4%
i 84669
 
4.5%
d 81980
 
4.4%
l 81827
 
4.4%
n 81396
 
4.4%
Other values (20) 399110
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1861577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 308435
16.6%
a 235106
12.6%
r 177715
9.5%
161959
8.7%
h 148722
 
8.0%
e 100658
 
5.4%
i 84669
 
4.5%
d 81980
 
4.4%
l 81827
 
4.4%
n 81396
 
4.4%
Other values (20) 399110
21.4%

Main_Cause
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size19.8 MiB
Human Error
239548 
Not Applicable
53922 
Vehicle Defect
 
7163
Road Environment Defect
 
1124
Accident
 
1

Length

Max length23
Median length11
Mean length11.651971
Min length8

Characters and Unicode

Total characters3516087
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowHuman Error
2nd rowHuman Error
3rd rowHuman Error
4th rowHuman Error
5th rowHuman Error

Common Values

ValueCountFrequency (%)
Human Error 239548
79.4%
Not Applicable 53922
 
17.9%
Vehicle Defect 7163
 
2.4%
Road Environment Defect 1124
 
0.4%
Accident 1
 
< 0.1%
BUDDHISTS 1
 
< 0.1%
(Missing) 2
 
< 0.1%

Length

2024-04-14T10:57:21.907758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:22.075814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
human 239548
39.6%
error 239548
39.6%
not 53922
 
8.9%
applicable 53922
 
8.9%
defect 8287
 
1.4%
vehicle 7163
 
1.2%
road 1124
 
0.2%
environment 1124
 
0.2%
accident 1
 
< 0.1%
buddhists 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 719768
20.5%
302881
8.6%
o 295718
8.4%
a 294594
8.4%
n 242921
 
6.9%
m 240672
 
6.8%
E 240672
 
6.8%
H 239549
 
6.8%
u 239548
 
6.8%
l 115007
 
3.3%
Other values (20) 584757
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3516087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 719768
20.5%
302881
8.6%
o 295718
8.4%
a 294594
8.4%
n 242921
 
6.9%
m 240672
 
6.8%
E 240672
 
6.8%
H 239549
 
6.8%
u 239548
 
6.8%
l 115007
 
3.3%
Other values (20) 584757
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3516087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 719768
20.5%
302881
8.6%
o 295718
8.4%
a 294594
8.4%
n 242921
 
6.9%
m 240672
 
6.8%
E 240672
 
6.8%
H 239549
 
6.8%
u 239548
 
6.8%
l 115007
 
3.3%
Other values (20) 584757
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3516087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 719768
20.5%
302881
8.6%
o 295718
8.4%
a 294594
8.4%
n 242921
 
6.9%
m 240672
 
6.8%
E 240672
 
6.8%
H 239549
 
6.8%
u 239548
 
6.8%
l 115007
 
3.3%
Other values (20) 584757
16.6%

Hit_Run
Categorical

Distinct3
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size17.7 MiB
No
197497 
Not Applicable
54531 
Yes
49730 

Length

Max length14
Median length2
Mean length4.3333333
Min length2

Characters and Unicode

Total characters1307618
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
No 197497
65.4%
Not Applicable 54531
 
18.1%
Yes 49730
 
16.5%
(Missing) 3
 
< 0.1%

Length

2024-04-14T10:57:22.296929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:22.460082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
no 197497
55.4%
not 54531
 
15.3%
applicable 54531
 
15.3%
yes 49730
 
14.0%

Most occurring characters

ValueCountFrequency (%)
N 252028
19.3%
o 252028
19.3%
p 109062
8.3%
l 109062
8.3%
e 104261
8.0%
t 54531
 
4.2%
54531
 
4.2%
A 54531
 
4.2%
i 54531
 
4.2%
c 54531
 
4.2%
Other values (4) 208522
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1307618
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 252028
19.3%
o 252028
19.3%
p 109062
8.3%
l 109062
8.3%
e 104261
8.0%
t 54531
 
4.2%
54531
 
4.2%
A 54531
 
4.2%
i 54531
 
4.2%
c 54531
 
4.2%
Other values (4) 208522
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1307618
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 252028
19.3%
o 252028
19.3%
p 109062
8.3%
l 109062
8.3%
e 104261
8.0%
t 54531
 
4.2%
54531
 
4.2%
A 54531
 
4.2%
i 54531
 
4.2%
c 54531
 
4.2%
Other values (4) 208522
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1307618
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 252028
19.3%
o 252028
19.3%
p 109062
8.3%
l 109062
8.3%
e 104261
8.0%
t 54531
 
4.2%
54531
 
4.2%
A 54531
 
4.2%
i 54531
 
4.2%
c 54531
 
4.2%
Other values (4) 208522
15.9%

Severity
Categorical

Distinct11
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size19.8 MiB
Grievous Injury
128846 
Simple Injury
70954 
Fatal
68604 
Damage Only
24607 
Not Applicable
 
8741
Other values (6)
 
7

Length

Max length15
Median length14
Mean length11.900918
Min length4

Characters and Unicode

Total characters3591209
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowGrievous Injury
2nd rowFatal
3rd rowDamage Only
4th rowDamage Only
5th rowFatal

Common Values

ValueCountFrequency (%)
Grievous Injury 128846
42.7%
Simple Injury 70954
23.5%
Fatal 68604
22.7%
Damage Only 24607
 
8.2%
Not Applicable 8741
 
2.9%
BUDDHISTS 2
 
< 0.1%
ACHARI 1
 
< 0.1%
Roof 1
 
< 0.1%
Father 1
 
< 0.1%
MEDARA 1
 
< 0.1%
(Missing) 2
 
< 0.1%

Length

2024-04-14T10:57:22.650399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
injury 199800
37.4%
grievous 128846
24.1%
simple 70954
 
13.3%
fatal 68604
 
12.8%
damage 24607
 
4.6%
only 24607
 
4.6%
not 8741
 
1.6%
applicable 8741
 
1.6%
buddhists 2
 
< 0.1%
achari 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 328648
 
9.2%
u 328646
 
9.2%
e 233150
 
6.5%
233148
 
6.5%
n 224407
 
6.2%
y 224407
 
6.2%
i 208541
 
5.8%
I 199803
 
5.6%
j 199800
 
5.6%
a 195164
 
5.4%
Other values (27) 1215495
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3591209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 328648
 
9.2%
u 328646
 
9.2%
e 233150
 
6.5%
233148
 
6.5%
n 224407
 
6.2%
y 224407
 
6.2%
i 208541
 
5.8%
I 199803
 
5.6%
j 199800
 
5.6%
a 195164
 
5.4%
Other values (27) 1215495
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3591209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 328648
 
9.2%
u 328646
 
9.2%
e 233150
 
6.5%
233148
 
6.5%
n 224407
 
6.2%
y 224407
 
6.2%
i 208541
 
5.8%
I 199803
 
5.6%
j 199800
 
5.6%
a 195164
 
5.4%
Other values (27) 1215495
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3591209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 328648
 
9.2%
u 328646
 
9.2%
e 233150
 
6.5%
233148
 
6.5%
n 224407
 
6.2%
y 224407
 
6.2%
i 208541
 
5.8%
I 199803
 
5.6%
j 199800
 
5.6%
a 195164
 
5.4%
Other values (27) 1215495
33.8%

Collision_Type
Categorical

Distinct24
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size20.3 MiB
Vehicle to Vehicle
93323 
Not Applicable
54971 
Others
48781 
Hit pedestrian
43721 
Head on
13587 
Other values (19)
47377 

Length

Max length26
Median length25
Mean length13.567696
Min length5

Characters and Unicode

Total characters4094188
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowDrowned
2nd rowHit fixed object
3rd rowHit and Run
4th rowHead on
5th rowHead on

Common Values

ValueCountFrequency (%)
Vehicle to Vehicle 93323
30.9%
Not Applicable 54971
18.2%
Others 48781
16.2%
Hit pedestrian 43721
14.5%
Head on 13587
 
4.5%
Hit and Run 11236
 
3.7%
Hit bicyclist 7303
 
2.4%
Skidding or Self accident 7284
 
2.4%
Rear end 6894
 
2.3%
Hit animal 2737
 
0.9%
Other values (14) 11923
 
4.0%

Length

2024-04-14T10:57:22.857338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vehicle 187520
27.7%
to 93323
13.8%
hit 67863
 
10.0%
not 54971
 
8.1%
applicable 54971
 
8.1%
others 48781
 
7.2%
pedestrian 43721
 
6.5%
head 13587
 
2.0%
on 13587
 
2.0%
run 11725
 
1.7%
Other values (30) 86375
12.8%

Most occurring characters

ValueCountFrequency (%)
e 622330
15.2%
i 408071
10.0%
374666
 
9.2%
t 330803
 
8.1%
l 318749
 
7.8%
c 275246
 
6.7%
h 238802
 
5.8%
V 186646
 
4.6%
o 176037
 
4.3%
p 158269
 
3.9%
Other values (29) 1004569
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4094188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 622330
15.2%
i 408071
10.0%
374666
 
9.2%
t 330803
 
8.1%
l 318749
 
7.8%
c 275246
 
6.7%
h 238802
 
5.8%
V 186646
 
4.6%
o 176037
 
4.3%
p 158269
 
3.9%
Other values (29) 1004569
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4094188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 622330
15.2%
i 408071
10.0%
374666
 
9.2%
t 330803
 
8.1%
l 318749
 
7.8%
c 275246
 
6.7%
h 238802
 
5.8%
V 186646
 
4.6%
o 176037
 
4.3%
p 158269
 
3.9%
Other values (29) 1004569
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4094188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 622330
15.2%
i 408071
10.0%
374666
 
9.2%
t 330803
 
8.1%
l 318749
 
7.8%
c 275246
 
6.7%
h 238802
 
5.8%
V 186646
 
4.6%
o 176037
 
4.3%
p 158269
 
3.9%
Other values (29) 1004569
24.5%

Junction_Control
Categorical

Distinct18
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size20.3 MiB
Not Applicable
103240 
Uncontrolled
71993 
Not at Junction
52255 
Controlled
43118 
No signal lights
16381 
Other values (13)
14773 

Length

Max length25
Median length23
Mean length13.50392
Min length3

Characters and Unicode

Total characters4074943
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowGive way sign

Common Values

ValueCountFrequency (%)
Not Applicable 103240
34.2%
Uncontrolled 71993
23.9%
Not at Junction 52255
17.3%
Controlled 43118
14.3%
No signal lights 16381
 
5.4%
Police / Manual 4543
 
1.5%
Give way sign 3235
 
1.1%
Signal lights Automatic 2529
 
0.8%
Stop sign 1512
 
0.5%
Signal lights Blinking 864
 
0.3%
Other values (8) 2090
 
0.7%

Length

2024-04-14T10:57:23.047137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
not 155971
27.4%
applicable 103240
18.1%
uncontrolled 71993
12.6%
junction 52256
 
9.2%
at 52255
 
9.2%
controlled 43118
 
7.6%
signal 20351
 
3.6%
lights 20351
 
3.6%
no 16381
 
2.9%
sign 5243
 
0.9%
Other values (13) 28108
 
4.9%

Most occurring characters

ValueCountFrequency (%)
l 488782
12.0%
o 465087
11.4%
t 403010
9.9%
n 326087
 
8.0%
319763
 
7.8%
c 234561
 
5.8%
e 226129
 
5.5%
i 215668
 
5.3%
p 208488
 
5.1%
a 192538
 
4.7%
Other values (31) 994830
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4074943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 488782
12.0%
o 465087
11.4%
t 403010
9.9%
n 326087
 
8.0%
319763
 
7.8%
c 234561
 
5.8%
e 226129
 
5.5%
i 215668
 
5.3%
p 208488
 
5.1%
a 192538
 
4.7%
Other values (31) 994830
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4074943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 488782
12.0%
o 465087
11.4%
t 403010
9.9%
n 326087
 
8.0%
319763
 
7.8%
c 234561
 
5.8%
e 226129
 
5.5%
i 215668
 
5.3%
p 208488
 
5.1%
a 192538
 
4.7%
Other values (31) 994830
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4074943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 488782
12.0%
o 465087
11.4%
t 403010
9.9%
n 326087
 
8.0%
319763
 
7.8%
c 234561
 
5.8%
e 226129
 
5.5%
i 215668
 
5.3%
p 208488
 
5.1%
a 192538
 
4.7%
Other values (31) 994830
24.4%

Road_Character
Categorical

Distinct13
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size19.6 MiB
Others
93529 
Straight and flat
87348 
Not Applicable
61495 
Curve
38613 
Hump
 
5482
Other values (8)
15290 

Length

Max length23
Median length22
Mean length10.942633
Min length4

Characters and Unicode

Total characters3302016
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCurve
2nd rowOthers
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Others 93529
31.0%
Straight and flat 87348
28.9%
Not Applicable 61495
20.4%
Curve 38613
12.8%
Hump 5482
 
1.8%
Incline 4797
 
1.6%
Slight Curve 3913
 
1.3%
Curve and Incline 2455
 
0.8%
Sharp Curve 1792
 
0.6%
Crest of hill 759
 
0.3%
Other values (3) 1574
 
0.5%

Length

2024-04-14T10:57:23.254952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
others 93529
16.9%
and 89803
16.2%
straight 87348
15.8%
flat 87348
15.8%
not 61495
11.1%
applicable 61495
11.1%
curve 46773
8.4%
incline 8074
 
1.5%
hump 5482
 
1.0%
slight 3913
 
0.7%
Other values (10) 8791
 
1.6%

Most occurring characters

ValueCountFrequency (%)
t 423314
12.8%
a 327786
 
9.9%
252294
 
7.6%
r 232527
 
7.0%
l 224959
 
6.8%
e 212274
 
6.4%
h 188093
 
5.7%
i 163163
 
4.9%
p 131544
 
4.0%
n 106245
 
3.2%
Other values (19) 1039817
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3302016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 423314
12.8%
a 327786
 
9.9%
252294
 
7.6%
r 232527
 
7.0%
l 224959
 
6.8%
e 212274
 
6.4%
h 188093
 
5.7%
i 163163
 
4.9%
p 131544
 
4.0%
n 106245
 
3.2%
Other values (19) 1039817
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3302016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 423314
12.8%
a 327786
 
9.9%
252294
 
7.6%
r 232527
 
7.0%
l 224959
 
6.8%
e 212274
 
6.4%
h 188093
 
5.7%
i 163163
 
4.9%
p 131544
 
4.0%
n 106245
 
3.2%
Other values (19) 1039817
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3302016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 423314
12.8%
a 327786
 
9.9%
252294
 
7.6%
r 232527
 
7.0%
l 224959
 
6.8%
e 212274
 
6.4%
h 188093
 
5.7%
i 163163
 
4.9%
p 131544
 
4.0%
n 106245
 
3.2%
Other values (19) 1039817
31.5%

Road_Type
Categorical

Distinct18
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size18.8 MiB
NH
91494 
State Highway
62091 
Others
58839 
City or Town Road
20073 
Village Road
16638 
Other values (13)
52624 

Length

Max length19
Median length18
Mean length8.3848535
Min length2

Characters and Unicode

Total characters2530205
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowState Highway
2nd rowState Highway
3rd rowState Highway
4th rowState Highway
5th rowState Highway

Common Values

ValueCountFrequency (%)
NH 91494
30.3%
State Highway 62091
20.6%
Others 58839
19.5%
City or Town Road 20073
 
6.7%
Village Road 16638
 
5.5%
Two way 12815
 
4.2%
Not Applicable 7009
 
2.3%
One way 6157
 
2.0%
Major District Road 5594
 
1.9%
Residential Street 5583
 
1.9%
Other values (8) 15466
 
5.1%

Length

2024-04-14T10:57:23.461448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nh 91494
18.3%
highway 62091
12.4%
state 62091
12.4%
others 58839
11.8%
road 52767
10.5%
city 20073
 
4.0%
or 20073
 
4.0%
town 20073
 
4.0%
way 18972
 
3.8%
village 16638
 
3.3%
Other values (16) 77620
15.5%

Most occurring characters

ValueCountFrequency (%)
t 252457
 
10.0%
a 234895
 
9.3%
198972
 
7.9%
e 187731
 
7.4%
H 153585
 
6.1%
i 152652
 
6.0%
o 124328
 
4.9%
h 120930
 
4.8%
r 118715
 
4.7%
w 115163
 
4.6%
Other values (25) 870777
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2530205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 252457
 
10.0%
a 234895
 
9.3%
198972
 
7.9%
e 187731
 
7.4%
H 153585
 
6.1%
i 152652
 
6.0%
o 124328
 
4.9%
h 120930
 
4.8%
r 118715
 
4.7%
w 115163
 
4.6%
Other values (25) 870777
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2530205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 252457
 
10.0%
a 234895
 
9.3%
198972
 
7.9%
e 187731
 
7.4%
H 153585
 
6.1%
i 152652
 
6.0%
o 124328
 
4.9%
h 120930
 
4.8%
r 118715
 
4.7%
w 115163
 
4.6%
Other values (25) 870777
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2530205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 252457
 
10.0%
a 234895
 
9.3%
198972
 
7.9%
e 187731
 
7.4%
H 153585
 
6.1%
i 152652
 
6.0%
o 124328
 
4.9%
h 120930
 
4.8%
r 118715
 
4.7%
w 115163
 
4.6%
Other values (25) 870777
34.4%

Surface_Type
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size19.8 MiB
Bitumen(Tar)
245745 
Not Applicable
 
22759
Concrete
 
14967
Surfaced
 
6794
Kutcha
 
6172
Other values (2)
 
5322

Length

Max length14
Median length12
Mean length11.648945
Min length6

Characters and Unicode

Total characters3515174
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBitumen(Tar)
2nd rowBitumen(Tar)
3rd rowBitumen(Tar)
4th rowBitumen(Tar)
5th rowBitumen(Tar)

Common Values

ValueCountFrequency (%)
Bitumen(Tar) 245745
81.4%
Not Applicable 22759
 
7.5%
Concrete 14967
 
5.0%
Surfaced 6794
 
2.3%
Kutcha 6172
 
2.0%
Gravel 3044
 
1.0%
Metalled 2278
 
0.8%
(Missing) 2
 
< 0.1%

Length

2024-04-14T10:57:23.665784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:23.856194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
bitumen(tar 245745
75.7%
not 22759
 
7.0%
applicable 22759
 
7.0%
concrete 14967
 
4.6%
surfaced 6794
 
2.1%
kutcha 6172
 
1.9%
gravel 3044
 
0.9%
metalled 2278
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 312832
 
8.9%
t 291921
 
8.3%
a 286792
 
8.2%
r 270550
 
7.7%
i 268504
 
7.6%
n 260712
 
7.4%
u 258711
 
7.4%
B 245745
 
7.0%
) 245745
 
7.0%
T 245745
 
7.0%
Other values (19) 827917
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3515174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 312832
 
8.9%
t 291921
 
8.3%
a 286792
 
8.2%
r 270550
 
7.7%
i 268504
 
7.6%
n 260712
 
7.4%
u 258711
 
7.4%
B 245745
 
7.0%
) 245745
 
7.0%
T 245745
 
7.0%
Other values (19) 827917
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3515174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 312832
 
8.9%
t 291921
 
8.3%
a 286792
 
8.2%
r 270550
 
7.7%
i 268504
 
7.6%
n 260712
 
7.4%
u 258711
 
7.4%
B 245745
 
7.0%
) 245745
 
7.0%
T 245745
 
7.0%
Other values (19) 827917
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3515174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 312832
 
8.9%
t 291921
 
8.3%
a 286792
 
8.2%
r 270550
 
7.7%
i 268504
 
7.6%
n 260712
 
7.4%
u 258711
 
7.4%
B 245745
 
7.0%
) 245745
 
7.0%
T 245745
 
7.0%
Other values (19) 827917
23.6%

Surface_Condition
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size17.7 MiB
Dry
224187 
Others
42247 
Not Applicable
26331 
Wet
 
3268
Muddy
 
2723
Other values (2)
 
3004

Length

Max length17
Median length3
Mean length4.5057794
Min length3

Characters and Unicode

Total characters1359664
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDry
2nd rowNot Applicable
3rd rowDry
4th rowDry
5th rowDry

Common Values

ValueCountFrequency (%)
Dry 224187
74.3%
Others 42247
 
14.0%
Not Applicable 26331
 
8.7%
Wet 3268
 
1.1%
Muddy 2723
 
0.9%
Ditch or Potholed 2054
 
0.7%
Flooded 950
 
0.3%
(Missing) 1
 
< 0.1%

Length

2024-04-14T10:57:24.059122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:24.247530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
dry 224187
67.5%
others 42247
 
12.7%
not 26331
 
7.9%
applicable 26331
 
7.9%
wet 3268
 
1.0%
muddy 2723
 
0.8%
ditch 2054
 
0.6%
or 2054
 
0.6%
potholed 2054
 
0.6%
flooded 950
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 268488
19.7%
y 226910
16.7%
D 226241
16.6%
t 75954
 
5.6%
e 74850
 
5.5%
l 55666
 
4.1%
p 52662
 
3.9%
h 46355
 
3.4%
s 42247
 
3.1%
O 42247
 
3.1%
Other values (14) 248044
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1359664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 268488
19.7%
y 226910
16.7%
D 226241
16.6%
t 75954
 
5.6%
e 74850
 
5.5%
l 55666
 
4.1%
p 52662
 
3.9%
h 46355
 
3.4%
s 42247
 
3.1%
O 42247
 
3.1%
Other values (14) 248044
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1359664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 268488
19.7%
y 226910
16.7%
D 226241
16.6%
t 75954
 
5.6%
e 74850
 
5.5%
l 55666
 
4.1%
p 52662
 
3.9%
h 46355
 
3.4%
s 42247
 
3.1%
O 42247
 
3.1%
Other values (14) 248044
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1359664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 268488
19.7%
y 226910
16.7%
D 226241
16.6%
t 75954
 
5.6%
e 74850
 
5.5%
l 55666
 
4.1%
p 52662
 
3.9%
h 46355
 
3.4%
s 42247
 
3.1%
O 42247
 
3.1%
Other values (14) 248044
18.2%

Road_Condition
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size21.1 MiB
Not Applicable
220980 
No influence on accident
64621 
Pot holed
 
5276
Construction Work / Material
 
4408
Engineering Defect of Road
 
3735

Length

Max length28
Median length14
Mean length16.407101
Min length9

Characters and Unicode

Total characters4950974
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable 220980
73.2%
No influence on accident 64621
 
21.4%
Pot holed 5276
 
1.7%
Construction Work / Material 4408
 
1.5%
Engineering Defect of Road 3735
 
1.2%
Drainage Ditch 2738
 
0.9%
(Missing) 3
 
< 0.1%

Length

2024-04-14T10:57:24.459255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:24.633062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
not 220980
29.5%
applicable 220980
29.5%
no 64621
 
8.6%
influence 64621
 
8.6%
on 64621
 
8.6%
accident 64621
 
8.6%
pot 5276
 
0.7%
holed 5276
 
0.7%
4408
 
0.6%
material 4408
 
0.6%
Other values (8) 29232
 
3.9%

Most occurring characters

ValueCountFrequency (%)
l 516265
10.4%
447286
9.0%
e 442205
8.9%
p 441960
8.9%
c 425724
8.6%
o 381468
 
7.7%
i 371984
 
7.5%
t 310574
 
6.3%
a 303628
 
6.1%
N 285601
 
5.8%
Other values (19) 1024279
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4950974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 516265
10.4%
447286
9.0%
e 442205
8.9%
p 441960
8.9%
c 425724
8.6%
o 381468
 
7.7%
i 371984
 
7.5%
t 310574
 
6.3%
a 303628
 
6.1%
N 285601
 
5.8%
Other values (19) 1024279
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4950974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 516265
10.4%
447286
9.0%
e 442205
8.9%
p 441960
8.9%
c 425724
8.6%
o 381468
 
7.7%
i 371984
 
7.5%
t 310574
 
6.3%
a 303628
 
6.1%
N 285601
 
5.8%
Other values (19) 1024279
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4950974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 516265
10.4%
447286
9.0%
e 442205
8.9%
p 441960
8.9%
c 425724
8.6%
o 381468
 
7.7%
i 371984
 
7.5%
t 310574
 
6.3%
a 303628
 
6.1%
N 285601
 
5.8%
Other values (19) 1024279
20.7%

Weather
Categorical

Distinct17
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size17.9 MiB
Clear
129320 
Fine
97032 
Others
43773 
Not Applicable
 
11906
Cloudy
 
7699
Other values (12)
 
12030

Length

Max length29
Median length14
Mean length5.355733
Min length4

Characters and Unicode

Total characters1616146
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowLight Rain
3rd rowClear
4th rowClear
5th rowClear

Common Values

ValueCountFrequency (%)
Clear 129320
42.9%
Fine 97032
32.2%
Others 43773
 
14.5%
Not Applicable 11906
 
3.9%
Cloudy 7699
 
2.6%
Very Hot 2832
 
0.9%
Light Rain 2226
 
0.7%
Very Cold 1732
 
0.6%
Fog / Mist 1259
 
0.4%
Snow 1140
 
0.4%
Other values (7) 2841
 
0.9%

Length

2024-04-14T10:57:24.846249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 129320
39.7%
fine 97032
29.8%
others 43773
 
13.4%
not 11906
 
3.7%
applicable 11906
 
3.7%
cloudy 7699
 
2.4%
very 4564
 
1.4%
hot 2832
 
0.9%
rain 2722
 
0.8%
light 2226
 
0.7%
Other values (16) 11989
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 287477
17.8%
r 179131
11.1%
l 163269
10.1%
a 144717
9.0%
C 138751
8.6%
i 117174
7.3%
n 102667
 
6.4%
F 99043
 
6.1%
t 64266
 
4.0%
s 46580
 
2.9%
Other values (25) 273071
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 287477
17.8%
r 179131
11.1%
l 163269
10.1%
a 144717
9.0%
C 138751
8.6%
i 117174
7.3%
n 102667
 
6.4%
F 99043
 
6.1%
t 64266
 
4.0%
s 46580
 
2.9%
Other values (25) 273071
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 287477
17.8%
r 179131
11.1%
l 163269
10.1%
a 144717
9.0%
C 138751
8.6%
i 117174
7.3%
n 102667
 
6.4%
F 99043
 
6.1%
t 64266
 
4.0%
s 46580
 
2.9%
Other values (25) 273071
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 287477
17.8%
r 179131
11.1%
l 163269
10.1%
a 144717
9.0%
C 138751
8.6%
i 117174
7.3%
n 102667
 
6.4%
F 99043
 
6.1%
t 64266
 
4.0%
s 46580
 
2.9%
Other values (25) 273071
16.9%

Lane_Type
Categorical

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing239253
Missing (%)79.3%
Memory size16.6 MiB
DualLane
25007 
Others
17293 
SingleLane
14628 
FourLane
4511 
SixLane
 
831

Length

Max length12
Median length10
Mean length7.9166667
Min length6

Characters and Unicode

Total characters494855
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDualLane
2nd rowDualLane
3rd rowDualLane
4th rowSingleLane
5th rowDualLane

Common Values

ValueCountFrequency (%)
DualLane 25007
 
8.3%
Others 17293
 
5.7%
SingleLane 14628
 
4.8%
FourLane 4511
 
1.5%
SixLane 831
 
0.3%
Intermediate 238
 
0.1%
(Missing) 239253
79.3%

Length

2024-04-14T10:57:25.031321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:25.239460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
duallane 25007
40.0%
others 17293
27.7%
singlelane 14628
23.4%
fourlane 4511
 
7.2%
sixlane 831
 
1.3%
intermediate 238
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 77612
15.7%
a 70222
14.2%
n 59843
12.1%
L 44977
9.1%
l 39635
8.0%
u 29518
 
6.0%
D 25007
 
5.1%
r 22042
 
4.5%
t 17769
 
3.6%
h 17293
 
3.5%
Other values (11) 90937
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 77612
15.7%
a 70222
14.2%
n 59843
12.1%
L 44977
9.1%
l 39635
8.0%
u 29518
 
6.0%
D 25007
 
5.1%
r 22042
 
4.5%
t 17769
 
3.6%
h 17293
 
3.5%
Other values (11) 90937
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 77612
15.7%
a 70222
14.2%
n 59843
12.1%
L 44977
9.1%
l 39635
8.0%
u 29518
 
6.0%
D 25007
 
5.1%
r 22042
 
4.5%
t 17769
 
3.6%
h 17293
 
3.5%
Other values (11) 90937
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 77612
15.7%
a 70222
14.2%
n 59843
12.1%
L 44977
9.1%
l 39635
8.0%
u 29518
 
6.0%
D 25007
 
5.1%
r 22042
 
4.5%
t 17769
 
3.6%
h 17293
 
3.5%
Other values (11) 90937
18.4%

Road_Markings
Categorical

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing271601
Missing (%)90.0%
Memory size16.6 MiB
Centre White Line
17594 
Centre Broken Line
7145 
Kerb Line
2285 
Directional Marking
 
1433
Zebra Crossing
 
863
Other values (2)
 
840

Length

Max length19
Median length17
Mean length16.667042
Min length6

Characters and Unicode

Total characters502678
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentre White Line
2nd rowCentre White Line
3rd rowCentre White Line
4th rowCentre Yellow Line
5th rowCentre White Line

Common Values

ValueCountFrequency (%)
Centre White Line 17594
 
5.8%
Centre Broken Line 7145
 
2.4%
Kerb Line 2285
 
0.8%
Directional Marking 1433
 
0.5%
Zebra Crossing 863
 
0.3%
Centre Yellow Line 838
 
0.3%
ACHARI 2
 
< 0.1%
(Missing) 271601
90.0%

Length

2024-04-14T10:57:25.444163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:25.639286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
line 27862
32.4%
centre 25577
29.8%
white 17594
20.5%
broken 7145
 
8.3%
kerb 2285
 
2.7%
directional 1433
 
1.7%
marking 1433
 
1.7%
zebra 863
 
1.0%
crossing 863
 
1.0%
yellow 838
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 109174
21.7%
n 64313
12.8%
55735
11.1%
i 50618
10.1%
t 44604
8.9%
r 39599
 
7.9%
L 27862
 
5.5%
C 26442
 
5.3%
W 17594
 
3.5%
h 17594
 
3.5%
Other values (19) 49143
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 502678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 109174
21.7%
n 64313
12.8%
55735
11.1%
i 50618
10.1%
t 44604
8.9%
r 39599
 
7.9%
L 27862
 
5.5%
C 26442
 
5.3%
W 17594
 
3.5%
h 17594
 
3.5%
Other values (19) 49143
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 502678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 109174
21.7%
n 64313
12.8%
55735
11.1%
i 50618
10.1%
t 44604
8.9%
r 39599
 
7.9%
L 27862
 
5.5%
C 26442
 
5.3%
W 17594
 
3.5%
h 17594
 
3.5%
Other values (19) 49143
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 502678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 109174
21.7%
n 64313
12.8%
55735
11.1%
i 50618
10.1%
t 44604
8.9%
r 39599
 
7.9%
L 27862
 
5.5%
C 26442
 
5.3%
W 17594
 
3.5%
h 17594
 
3.5%
Other values (19) 49143
9.8%

Spot_Conditions
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing248412
Missing (%)82.3%
Memory size16.5 MiB
Others
49158 
Tree or Branches
 
2064
Construction Work or Materials
 
1073
Electric or Telephone or Pole or Cable
 
1052
Father
 
1

Length

Max length38
Median length6
Mean length7.5006654
Min length6

Characters and Unicode

Total characters400153
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowOthers
2nd rowOthers
3rd rowOthers
4th rowOthers
5th rowOthers

Common Values

ValueCountFrequency (%)
Others 49158
 
16.3%
Tree or Branches 2064
 
0.7%
Construction Work or Materials 1073
 
0.4%
Electric or Telephone or Pole or Cable 1052
 
0.3%
Father 1
 
< 0.1%
BUDDHISTS 1
 
< 0.1%
(Missing) 248412
82.3%

Length

2024-04-14T10:57:25.852591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:26.026239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
others 49158
73.4%
or 6293
 
9.4%
tree 2064
 
3.1%
branches 2064
 
3.1%
construction 1073
 
1.6%
work 1073
 
1.6%
materials 1073
 
1.6%
electric 1052
 
1.6%
telephone 1052
 
1.6%
pole 1052
 
1.6%
Other values (3) 1054
 
1.6%

Most occurring characters

ValueCountFrequency (%)
r 63851
16.0%
e 62736
15.7%
t 53430
13.4%
s 53368
13.3%
h 52275
13.1%
O 49158
12.3%
13659
 
3.4%
o 11616
 
2.9%
l 5281
 
1.3%
a 5263
 
1.3%
Other values (20) 29516
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 63851
16.0%
e 62736
15.7%
t 53430
13.4%
s 53368
13.3%
h 52275
13.1%
O 49158
12.3%
13659
 
3.4%
o 11616
 
2.9%
l 5281
 
1.3%
a 5263
 
1.3%
Other values (20) 29516
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 63851
16.0%
e 62736
15.7%
t 53430
13.4%
s 53368
13.3%
h 52275
13.1%
O 49158
12.3%
13659
 
3.4%
o 11616
 
2.9%
l 5281
 
1.3%
a 5263
 
1.3%
Other values (20) 29516
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 63851
16.0%
e 62736
15.7%
t 53430
13.4%
s 53368
13.3%
h 52275
13.1%
O 49158
12.3%
13659
 
3.4%
o 11616
 
2.9%
l 5281
 
1.3%
a 5263
 
1.3%
Other values (20) 29516
7.4%

Side_Walk
Categorical

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing254704
Missing (%)84.4%
Memory size16.6 MiB
Not Applicable
15287 
No Side Walk
14346 
Paved
7963 
Concrete
6703 
Asphalted
1562 
Other values (2)
 
1196

Length

Max length14
Median length12
Mean length10.694179
Min length5

Characters and Unicode

Total characters503236
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAsphalted
2nd rowAsphalted
3rd rowAsphalted
4th rowNot Applicable
5th rowAsphalted

Common Values

ValueCountFrequency (%)
Not Applicable 15287
 
5.1%
No Side Walk 14346
 
4.8%
Paved 7963
 
2.6%
Concrete 6703
 
2.2%
Asphalted 1562
 
0.5%
Metalled 1195
 
0.4%
BUDDHISTS 1
 
< 0.1%
(Missing) 254704
84.4%

Length

2024-04-14T10:57:26.230812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T10:57:26.387867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
not 15287
16.8%
applicable 15287
16.8%
no 14346
15.8%
side 14346
15.8%
walk 14346
15.8%
paved 7963
8.7%
concrete 6703
7.4%
asphalted 1562
 
1.7%
metalled 1195
 
1.3%
buddhists 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 54954
 
10.9%
l 48872
 
9.7%
43979
 
8.7%
a 40353
 
8.0%
o 36336
 
7.2%
p 32136
 
6.4%
N 29633
 
5.9%
i 29633
 
5.9%
d 25066
 
5.0%
t 24747
 
4.9%
Other values (20) 137527
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 503236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 54954
 
10.9%
l 48872
 
9.7%
43979
 
8.7%
a 40353
 
8.0%
o 36336
 
7.2%
p 32136
 
6.4%
N 29633
 
5.9%
i 29633
 
5.9%
d 25066
 
5.0%
t 24747
 
4.9%
Other values (20) 137527
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 503236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 54954
 
10.9%
l 48872
 
9.7%
43979
 
8.7%
a 40353
 
8.0%
o 36336
 
7.2%
p 32136
 
6.4%
N 29633
 
5.9%
i 29633
 
5.9%
d 25066
 
5.0%
t 24747
 
4.9%
Other values (20) 137527
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 503236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 54954
 
10.9%
l 48872
 
9.7%
43979
 
8.7%
a 40353
 
8.0%
o 36336
 
7.2%
p 32136
 
6.4%
N 29633
 
5.9%
i 29633
 
5.9%
d 25066
 
5.0%
t 24747
 
4.9%
Other values (20) 137527
27.3%

RoadJunction
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing301761
Missing (%)100.0%
Memory size2.3 MiB

Collision_TypeB
Categorical

MISSING 

Distinct17
Distinct (%)< 0.1%
Missing125075
Missing (%)41.4%
Memory size18.0 MiB
Others
68117 
Head on
26194 
Rear end
12280 
Skidding or Self accident
12175 
Hit and Run
11476 
Other values (12)
46444 

Length

Max length26
Median length25
Mean length10.049495
Min length6

Characters and Unicode

Total characters1775605
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOthers
2nd rowOthers
3rd rowOthers
4th rowNot Applicable
5th rowOthers

Common Values

ValueCountFrequency (%)
Others 68117
22.6%
Head on 26194
 
8.7%
Rear end 12280
 
4.1%
Skidding or Self accident 12175
 
4.0%
Hit and Run 11476
 
3.8%
Not Applicable 10811
 
3.6%
Not Applicable 9847
 
3.3%
Overturning 5435
 
1.8%
Side swipe 4592
 
1.5%
Run Off Road 4159
 
1.4%
Other values (7) 11600
 
3.8%
(Missing) 125075
41.4%

Length

2024-04-14T10:57:26.612184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
others 68117
20.6%
head 26194
 
7.9%
on 26194
 
7.9%
not 20658
 
6.2%
applicable 20658
 
6.2%
hit 16813
 
5.1%
run 15635
 
4.7%
or 14466
 
4.4%
rear 12280
 
3.7%
end 12280
 
3.7%
Other values (22) 98161
29.6%

Most occurring characters

ValueCountFrequency (%)
e 196128
 
11.0%
164617
 
9.3%
t 133269
 
7.5%
r 111780
 
6.3%
n 108948
 
6.1%
i 106118
 
6.0%
d 104599
 
5.9%
a 94351
 
5.3%
O 77711
 
4.4%
s 75341
 
4.2%
Other values (24) 602743
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1775605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 196128
 
11.0%
164617
 
9.3%
t 133269
 
7.5%
r 111780
 
6.3%
n 108948
 
6.1%
i 106118
 
6.0%
d 104599
 
5.9%
a 94351
 
5.3%
O 77711
 
4.4%
s 75341
 
4.2%
Other values (24) 602743
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1775605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 196128
 
11.0%
164617
 
9.3%
t 133269
 
7.5%
r 111780
 
6.3%
n 108948
 
6.1%
i 106118
 
6.0%
d 104599
 
5.9%
a 94351
 
5.3%
O 77711
 
4.4%
s 75341
 
4.2%
Other values (24) 602743
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1775605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 196128
 
11.0%
164617
 
9.3%
t 133269
 
7.5%
r 111780
 
6.3%
n 108948
 
6.1%
i 106118
 
6.0%
d 104599
 
5.9%
a 94351
 
5.3%
O 77711
 
4.4%
s 75341
 
4.2%
Other values (24) 602743
33.9%
Distinct157085
Distinct (%)52.1%
Missing123
Missing (%)< 0.1%
Memory size22.9 MiB
2024-04-14T10:57:26.989051image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length100
Median length85
Mean length22.491324
Min length1

Characters and Unicode

Total characters6784238
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134221 ?
Unique (%)44.5%

Sample

1st rowAMINAGAD BAGALKOT SGH-20 ROAD NEAR TIPPANNA GOUDAR FIELED
2nd rowSHIRUR AMINAGAD SH-20 ROAD NEAR KAMATAGI
3rd rowAMINAGAD TO BAGALKOT SH-20 ROAD NEAR BANATHIKOLLA
4th rowAMINAGAD BAGALKOT ROAD NEAR ADILASHA HOTEL
5th rowAMD TO BGK SH-20 ROAD NEAR AMINGAD SULEBAVI CROSS
ValueCountFrequency (%)
road 185148
 
17.7%
near 46664
 
4.4%
nh 31928
 
3.0%
village 20090
 
1.9%
main 19660
 
1.9%
to 12706
 
1.2%
cross 12148
 
1.2%
8204
 
0.8%
other 7314
 
0.7%
highway 7159
 
0.7%
Other values (78022) 697893
66.5%
2024-04-14T10:57:27.568525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
757812
 
11.2%
a 645418
 
9.5%
A 455142
 
6.7%
R 339105
 
5.0%
r 241379
 
3.6%
N 233395
 
3.4%
o 218160
 
3.2%
d 198986
 
2.9%
i 187331
 
2.8%
H 185048
 
2.7%
Other values (73) 3322462
49.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6784238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
757812
 
11.2%
a 645418
 
9.5%
A 455142
 
6.7%
R 339105
 
5.0%
r 241379
 
3.6%
N 233395
 
3.4%
o 218160
 
3.2%
d 198986
 
2.9%
i 187331
 
2.8%
H 185048
 
2.7%
Other values (73) 3322462
49.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6784238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
757812
 
11.2%
a 645418
 
9.5%
A 455142
 
6.7%
R 339105
 
5.0%
r 241379
 
3.6%
N 233395
 
3.4%
o 218160
 
3.2%
d 198986
 
2.9%
i 187331
 
2.8%
H 185048
 
2.7%
Other values (73) 3322462
49.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6784238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
757812
 
11.2%
a 645418
 
9.5%
A 455142
 
6.7%
R 339105
 
5.0%
r 241379
 
3.6%
N 233395
 
3.4%
o 218160
 
3.2%
d 198986
 
2.9%
i 187331
 
2.8%
H 185048
 
2.7%
Other values (73) 3322462
49.0%
Distinct156356
Distinct (%)51.9%
Missing568
Missing (%)0.2%
Memory size20.9 MiB
2024-04-14T10:57:27.992584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length139
Median length104
Mean length15.742733
Min length1

Characters and Unicode

Total characters4741601
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135321 ?
Unique (%)44.9%

Sample

1st row8
2nd row14
3rd rowBANATHIKOLLA
4th row500 MITER
5th row100MM
ValueCountFrequency (%)
near 77910
 
9.8%
road 33160
 
4.2%
1 20013
 
2.5%
village 16409
 
2.1%
cross 12899
 
1.6%
pole 10473
 
1.3%
bus 9893
 
1.2%
gate 9353
 
1.2%
temple 7345
 
0.9%
house 7153
 
0.9%
Other values (67693) 590323
74.3%
2024-04-14T10:57:28.622135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
499921
 
10.5%
a 402160
 
8.5%
A 273981
 
5.8%
e 208458
 
4.4%
r 188258
 
4.0%
N 173915
 
3.7%
R 165888
 
3.5%
l 151587
 
3.2%
E 149354
 
3.1%
o 135712
 
2.9%
Other values (56) 2392367
50.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4741601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
499921
 
10.5%
a 402160
 
8.5%
A 273981
 
5.8%
e 208458
 
4.4%
r 188258
 
4.0%
N 173915
 
3.7%
R 165888
 
3.5%
l 151587
 
3.2%
E 149354
 
3.1%
o 135712
 
2.9%
Other values (56) 2392367
50.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4741601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
499921
 
10.5%
a 402160
 
8.5%
A 273981
 
5.8%
e 208458
 
4.4%
r 188258
 
4.0%
N 173915
 
3.7%
R 165888
 
3.5%
l 151587
 
3.2%
E 149354
 
3.1%
o 135712
 
2.9%
Other values (56) 2392367
50.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4741601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
499921
 
10.5%
a 402160
 
8.5%
A 273981
 
5.8%
e 208458
 
4.4%
r 188258
 
4.0%
N 173915
 
3.7%
R 165888
 
3.5%
l 151587
 
3.2%
E 149354
 
3.1%
o 135712
 
2.9%
Other values (56) 2392367
50.5%

landmark_second
Text

MISSING 

Distinct23158
Distinct (%)50.4%
Missing255792
Missing (%)84.8%
Memory size10.9 MiB
2024-04-14T10:57:28.971670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length98
Median length77
Mean length12.72997
Min length1

Characters and Unicode

Total characters585184
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20368 ?
Unique (%)44.3%

Sample

1st row10
2nd row14
3rd row1
4th row3
5th row6
ValueCountFrequency (%)
near 4982
 
4.8%
road 3987
 
3.9%
pole 2785
 
2.7%
1 2631
 
2.6%
0 2107
 
2.1%
2 1983
 
1.9%
village 1720
 
1.7%
house 1541
 
1.5%
light 1172
 
1.1%
electric 1080
 
1.1%
Other values (15975) 78756
76.7%
2024-04-14T10:57:29.817521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57974
 
9.9%
a 43216
 
7.4%
A 31615
 
5.4%
e 25097
 
4.3%
E 22530
 
3.9%
R 20346
 
3.5%
r 19545
 
3.3%
l 17915
 
3.1%
N 17370
 
3.0%
L 17085
 
2.9%
Other values (54) 312491
53.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 585184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
57974
 
9.9%
a 43216
 
7.4%
A 31615
 
5.4%
e 25097
 
4.3%
E 22530
 
3.9%
R 20346
 
3.5%
r 19545
 
3.3%
l 17915
 
3.1%
N 17370
 
3.0%
L 17085
 
2.9%
Other values (54) 312491
53.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 585184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
57974
 
9.9%
a 43216
 
7.4%
A 31615
 
5.4%
e 25097
 
4.3%
E 22530
 
3.9%
R 20346
 
3.5%
r 19545
 
3.3%
l 17915
 
3.1%
N 17370
 
3.0%
L 17085
 
2.9%
Other values (54) 312491
53.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 585184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
57974
 
9.9%
a 43216
 
7.4%
A 31615
 
5.4%
e 25097
 
4.3%
E 22530
 
3.9%
R 20346
 
3.5%
r 19545
 
3.3%
l 17915
 
3.1%
N 17370
 
3.0%
L 17085
 
2.9%
Other values (54) 312491
53.4%
Distinct6876
Distinct (%)2.3%
Missing304
Missing (%)0.1%
Memory size17.4 MiB
2024-04-14T10:57:30.159303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.5955642
Min length1

Characters and Unicode

Total characters1083908
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3058 ?
Unique (%)1.0%

Sample

1st row8
2nd row14
3rd row3
4th row500M
5th row100MM
ValueCountFrequency (%)
km 72542
 
17.0%
1 20995
 
4.9%
10 16505
 
3.9%
2 14376
 
3.4%
15 11552
 
2.7%
ft 10843
 
2.5%
5 10802
 
2.5%
3 10388
 
2.4%
kms 10062
 
2.4%
12 9453
 
2.2%
Other values (2745) 239481
56.1%
2024-04-14T10:57:30.670905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 149287
13.8%
126000
11.6%
1 113624
10.5%
M 96425
 
8.9%
K 92048
 
8.5%
5 72238
 
6.7%
2 68196
 
6.3%
m 56920
 
5.3%
3 32952
 
3.0%
k 32103
 
3.0%
Other values (57) 244115
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1083908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 149287
13.8%
126000
11.6%
1 113624
10.5%
M 96425
 
8.9%
K 92048
 
8.5%
5 72238
 
6.7%
2 68196
 
6.3%
m 56920
 
5.3%
3 32952
 
3.0%
k 32103
 
3.0%
Other values (57) 244115
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1083908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 149287
13.8%
126000
11.6%
1 113624
10.5%
M 96425
 
8.9%
K 92048
 
8.5%
5 72238
 
6.7%
2 68196
 
6.3%
m 56920
 
5.3%
3 32952
 
3.0%
k 32103
 
3.0%
Other values (57) 244115
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1083908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 149287
13.8%
126000
11.6%
1 113624
10.5%
M 96425
 
8.9%
K 92048
 
8.5%
5 72238
 
6.7%
2 68196
 
6.3%
m 56920
 
5.3%
3 32952
 
3.0%
k 32103
 
3.0%
Other values (57) 244115
22.5%
Distinct2142
Distinct (%)8.0%
Missing274895
Missing (%)91.1%
Memory size9.9 MiB
2024-04-14T10:57:31.009747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.0107199
Min length1

Characters and Unicode

Total characters80886
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1029 ?
Unique (%)3.8%

Sample

1st row20
2nd row4
3rd row3
4th row11KM
5th row80 MTR
ValueCountFrequency (%)
ft 1399
 
4.5%
1 1365
 
4.4%
50 1241
 
4.0%
30 1078
 
3.5%
20 1027
 
3.3%
100 945
 
3.1%
2 913
 
3.0%
km 900
 
2.9%
10 876
 
2.8%
40 850
 
2.7%
Other values (1001) 20334
65.7%
2024-04-14T10:57:31.575348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17055
21.1%
1 8125
 
10.0%
5 7368
 
9.1%
2 5888
 
7.3%
F 4160
 
5.1%
4072
 
5.0%
T 4051
 
5.0%
3 3438
 
4.3%
t 3189
 
3.9%
M 3174
 
3.9%
Other values (47) 20366
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17055
21.1%
1 8125
 
10.0%
5 7368
 
9.1%
2 5888
 
7.3%
F 4160
 
5.1%
4072
 
5.0%
T 4051
 
5.0%
3 3438
 
4.3%
t 3189
 
3.9%
M 3174
 
3.9%
Other values (47) 20366
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17055
21.1%
1 8125
 
10.0%
5 7368
 
9.1%
2 5888
 
7.3%
F 4160
 
5.1%
4072
 
5.0%
T 4051
 
5.0%
3 3438
 
4.3%
t 3189
 
3.9%
M 3174
 
3.9%
Other values (47) 20366
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17055
21.1%
1 8125
 
10.0%
5 7368
 
9.1%
2 5888
 
7.3%
F 4160
 
5.1%
4072
 
5.0%
T 4051
 
5.0%
3 3438
 
4.3%
t 3189
 
3.9%
M 3174
 
3.9%
Other values (47) 20366
25.2%
Distinct258958
Distinct (%)85.9%
Missing145
Missing (%)< 0.1%
Memory size240.5 MiB
2024-04-14T10:57:32.252192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6275
Median length2472
Mean length177.83286
Min length1

Characters and Unicode

Total characters53637236
Distinct characters274
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique253712 ?
Unique (%)84.1%

Sample

1st rowAMINAGADA TO BAGALKOT SH-20 ROAD NEAR TIPPANNA GOUDAR FIELED
2nd rowSHIRUR AMINAGAD SH-20 ROAD NEAR KAMATAGI
3rd rowAMINAGAD BAGALKOT SH-20 NEAR BANATHIKOLL
4th rowAMINAGAD BAGALKOT SH-20 ROAD NEAR ADILSHA HOTELA
5th rowAMD TO BGK SH-20 ROAD NEAR AMINAGAD SULEBAVI CROSS
ValueCountFrequency (%)
ರಸ್ತೆ 136214
 
1.9%
ಇರುತ್ತದೆ 106129
 
1.5%
ರಸ್ತೆಯ 90424
 
1.3%
ಅಡಿ 79515
 
1.1%
ಕಡೆಗೆ 73775
 
1.0%
71974
 
1.0%
ಅಪಘಾತ 69596
 
1.0%
road 61593
 
0.9%
ರಸ್ತೆಯಲ್ಲಿ 56394
 
0.8%
54873
 
0.8%
Other values (356260) 6414964
88.9%
2024-04-14T10:57:33.100614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7350426
 
13.7%
4168913
 
7.8%
2984909
 
5.6%
ಿ 2807560
 
5.2%
2252727
 
4.2%
2023159
 
3.8%
1903994
 
3.5%
1897229
 
3.5%
1879970
 
3.5%
1772069
 
3.3%
Other values (264) 24596280
45.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53637236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7350426
 
13.7%
4168913
 
7.8%
2984909
 
5.6%
ಿ 2807560
 
5.2%
2252727
 
4.2%
2023159
 
3.8%
1903994
 
3.5%
1897229
 
3.5%
1879970
 
3.5%
1772069
 
3.3%
Other values (264) 24596280
45.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53637236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7350426
 
13.7%
4168913
 
7.8%
2984909
 
5.6%
ಿ 2807560
 
5.2%
2252727
 
4.2%
2023159
 
3.8%
1903994
 
3.5%
1897229
 
3.5%
1879970
 
3.5%
1772069
 
3.3%
Other values (264) 24596280
45.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53637236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7350426
 
13.7%
4168913
 
7.8%
2984909
 
5.6%
ಿ 2807560
 
5.2%
2252727
 
4.2%
2023159
 
3.8%
1903994
 
3.5%
1897229
 
3.5%
1879970
 
3.5%
1772069
 
3.3%
Other values (264) 24596280
45.9%

Latitude
Real number (ℝ)

ZEROS 

Distinct91009
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5785584
Minimum0
Maximum131.42999
Zeros204549
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-04-14T10:57:33.324354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.941537
95-th percentile16.20098
Maximum131.42999
Range131.42999
Interquartile range (IQR)12.941537

Descriptive statistics

Standard deviation6.9603076
Coefficient of variation (CV)1.5201963
Kurtosis7.560761
Mean4.5785584
Median Absolute Deviation (MAD)0
Skewness1.5777491
Sum1381630.4
Variance48.445882
MonotonicityNot monotonic
2024-04-14T10:57:33.556090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 204549
67.8%
6.752783 246
 
0.1%
14 101
 
< 0.1%
77.553864 77
 
< 0.1%
13.2298289 61
 
< 0.1%
13.241716 57
 
< 0.1%
13.21992627 54
 
< 0.1%
13 51
 
< 0.1%
37.088342 50
 
< 0.1%
13.030265 50
 
< 0.1%
Other values (90999) 96465
32.0%
ValueCountFrequency (%)
0 204549
67.8%
0.0012 1
 
< 0.1%
0.1 1
 
< 0.1%
0.122 1
 
< 0.1%
0.1234567 1
 
< 0.1%
0.1255262 1
 
< 0.1%
0.1256489 1
 
< 0.1%
0.12565754 1
 
< 0.1%
0.125751 1
 
< 0.1%
0.12575321 1
 
< 0.1%
ValueCountFrequency (%)
131.42999 1
 
< 0.1%
77.9686414 1
 
< 0.1%
77.71312018 1
 
< 0.1%
77.69644797 1
 
< 0.1%
77.68386414 1
 
< 0.1%
77.6814521 3
< 0.1%
77.67611064 1
 
< 0.1%
77.6714521 1
 
< 0.1%
77.67122646 1
 
< 0.1%
77.66829989 1
 
< 0.1%

Longitude
Real number (ℝ)

ZEROS 

Distinct91160
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.353019
Minimum0
Maximum776.14339
Zeros204552
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-04-14T10:57:33.777969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q375.05879
95-th percentile77.544123
Maximum776.14339
Range776.14339
Interquartile range (IQR)75.05879

Descriptive statistics

Standard deviation35.572713
Coefficient of variation (CV)1.4607106
Kurtosis-0.73603112
Mean24.353019
Median Absolute Deviation (MAD)0
Skewness0.80663209
Sum7348791.3
Variance1265.4179
MonotonicityNot monotonic
2024-04-14T10:57:34.025641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 204552
67.8%
68.177512 162
 
0.1%
76 107
 
< 0.1%
97.412897 87
 
< 0.1%
12.4089459 81
 
< 0.1%
77 67
 
< 0.1%
77.702282 64
 
< 0.1%
77.713731 56
 
< 0.1%
77.67141724 54
 
< 0.1%
77.539309 49
 
< 0.1%
Other values (91150) 96482
32.0%
ValueCountFrequency (%)
0 204552
67.8%
0.1 1
 
< 0.1%
0.1234567 1
 
< 0.1%
0.125656 1
 
< 0.1%
0.46 1
 
< 0.1%
0.7700843 1
 
< 0.1%
0.77304877 1
 
< 0.1%
0.77314627 1
 
< 0.1%
0.77314634 1
 
< 0.1%
0.77315127 1
 
< 0.1%
ValueCountFrequency (%)
776.14339 1
 
< 0.1%
177.672668 1
 
< 0.1%
99.573812 1
 
< 0.1%
97.542 1
 
< 0.1%
97.42578 1
 
< 0.1%
97.412898 4
 
< 0.1%
97.412897 87
< 0.1%
97.41289 2
 
< 0.1%
97.412289 1
 
< 0.1%
97.352468 1
 
< 0.1%

Interactions

2024-04-14T10:57:09.590786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:04.989104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:06.584695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:07.587070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:08.622525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:09.795283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:05.800343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:06.775636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:07.803088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:08.796394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:09.995769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:05.989760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:06.949209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:07.992142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:09.001354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:10.190846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:06.184372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:07.155444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:08.198952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:09.189883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:10.394758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:06.384290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:07.345369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:08.401995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T10:57:09.390330image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-14T10:57:11.081864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T10:57:12.651998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DISTRICTNAMEUNITNAMECrime_NoYearRINoofvehicle_involvedAccident_ClassificationAccident_SpotAccident_LocationAccident_SubLocationAccident_SpotBMain_CauseHit_RunSeverityCollision_TypeJunction_ControlRoad_CharacterRoad_TypeSurface_TypeSurface_ConditionRoad_ConditionWeatherLane_TypeRoad_MarkingsSpot_ConditionsSide_WalkRoadJunctionCollision_TypeBAccident_RoadLandmark_firstlandmark_secondDistance_LandMark_FirstDistance_LandMark_SecondAccident_DescriptionLatitudeLongitude
0BagalkotAmengad PS10470124520160139201611Road AccidentsBottleneckRural AreasOpen areaNaNHuman ErrorNoGrievous InjuryDrownedNot ApplicableCurveState HighwayBitumen(Tar)DryNot ApplicableClearNaNNaNNaNAsphaltedNaNNaNAMINAGAD BAGALKOT SGH-20 ROAD NEAR TIPPANNA GOUDAR FIELED8NaN8NaNAMINAGADA TO BAGALKOT SH-20 ROAD NEAR TIPPANNA GOUDAR FIELED0.00.0
1BagalkotAmengad PS10470124520160143201611Road AccidentsBridgeVillages settlementNarrow bridge or culvertsNaNHuman ErrorNoFatalHit fixed objectNot ApplicableOthersState HighwayBitumen(Tar)Not ApplicableNot ApplicableLight RainDualLaneCentre White LineNaNAsphaltedNaNNaNSHIRUR AMINAGAD SH-20 ROAD NEAR KAMATAGI14NaN14NaNSHIRUR AMINAGAD SH-20 ROAD NEAR KAMATAGI0.00.0
2BagalkotAmengad PS10470124520160056201612Road AccidentsBottleneckCity/TownNear School or CollegeNaNHuman ErrorYesDamage OnlyHit and RunNot ApplicableNot ApplicableState HighwayBitumen(Tar)DryNot ApplicableClearDualLaneCentre White LineOthersAsphaltedNaNNaNAMINAGAD TO BAGALKOT SH-20 ROAD NEAR BANATHIKOLLABANATHIKOLLANaN3NaNAMINAGAD BAGALKOT SH-20 NEAR BANATHIKOLL0.00.0
3BagalkotAmengad PS10470124520160134201612Road AccidentsBottleneckRural AreasResidential areaNaNHuman ErrorNoDamage OnlyHead onNot ApplicableNot ApplicableState HighwayBitumen(Tar)DryNot ApplicableClearDualLaneCentre White LineOthersNaNNaNNaNAMINAGAD BAGALKOT ROAD NEAR ADILASHA HOTEL500 MITERNaN500MNaNAMINAGAD BAGALKOT SH-20 ROAD NEAR ADILSHA HOTELA0.00.0
4BagalkotAmengad PS10470124520160161201611Road AccidentsCross roadsCity/TownAt pedestrian CrossingNaNHuman ErrorYesFatalHead onGive way signNot ApplicableState HighwayBitumen(Tar)DryNot ApplicableClearNaNNaNNaNNot ApplicableNaNNaNAMD TO BGK SH-20 ROAD NEAR AMINGAD SULEBAVI CROSS100MMNaN100MMNaNAMD TO BGK SH-20 ROAD NEAR AMINAGAD SULEBAVI CROSS0.00.0
5BagalkotAmengad PS10470124520160063201611Road AccidentsNot ApplicableRural AreasNear or inside a villageNaNHuman ErrorNoGrievous InjuryHit animalNot ApplicableNot ApplicableVillage RoadBitumen(Tar)DryNot ApplicableClearSingleLaneNaNNaNAsphaltedNaNNaNGUDUR TO MURADI ROAD CHOUKI MADDICHOUKKI MADDINaN20NaNಗುಡೂರ ಮುರಡಿ ರೋಡ ಚೌಕಿ ಮಡ್ಡಿ ಹತ್ತಿರ0.00.0
6BagalkotAmengad PS10470124520160169201612Road AccidentsBottleneckRural AreasOpen areaNaNHuman ErrorNoDamage OnlyHit fixed objectNot ApplicableOthersState HighwayBitumen(Tar)DryNot ApplicableFineDualLaneCentre Yellow LineOthersAsphaltedNaNNaNAMINAGAD BAGALKOT SH -20 ROAD NEAR GOUDAR FILEID7NaN7NaNಅಮಿನಗಡ ಬಾಗಲಕೋಟ ೆಸ್ ಎಚ್-20 ರಸ್ತೆ ಮೇಲೆ ಗೌಡರ ಜಮೀನದ ಹತ್ತಿರ0.00.0
7BagalkotAmengad PS10470124520160011201612Not ApplicableBottleneckRural AreasNear School or CollegeNaNHuman ErrorNoGrievous InjuryRear endNot ApplicableNot ApplicableVillage RoadBitumen(Tar)OthersNot ApplicableFineOthersNaNNaNNaNNaNNaNILAL VADAGERI VILLAGE20NaN20NaNಇಲಾಳ ವಡಗೇರಿ ರಸ್ತೆ ಮೇಲೆ0.00.0
8BagalkotAmengad PS10470124520160058201611Road AccidentsMore than four armsCity/TownIn bazaarNaNHuman ErrorNoFatalHit fixed objectSignals (Working)OthersState HighwayBitumen(Tar)Not ApplicableNot ApplicableClearDualLaneCentre White LineOthersAsphaltedNaNNaNHUNAGUND TO AMINAGAD SH-20 ROAD NEAR KURI BAZARKURI BAZARNaN2 KMNaNHUNGUND TO AMINGAD SH-20 ROAD NEAR KURI BAZAR0.00.0
9BagalkotAmengad PS10470124520160093201611Road AccidentsRoad hump or Rumble stripsRural AreasNarrow bridge or culvertsNaNHuman ErrorNoFatalHead onNot at JunctionOthersOne wayBitumen(Tar)DryNot ApplicableClearNaNNaNOthersNaNNaNNaNAMINAGAD SANGAM ROAD5NaN5NaNಅಮೀನಗಡ ಸಂಗಮ ರೋಡ ಹುಲಗಿನಾಳ ಹತ್ತಿರ ಘಟಮಟ್ಟೇಶ್ವರ ದ್ವಾರ ಬಾಗಿಲು ಹತ್ತಿರ0.00.0
DISTRICTNAMEUNITNAMECrime_NoYearRINoofvehicle_involvedAccident_ClassificationAccident_SpotAccident_LocationAccident_SubLocationAccident_SpotBMain_CauseHit_RunSeverityCollision_TypeJunction_ControlRoad_CharacterRoad_TypeSurface_TypeSurface_ConditionRoad_ConditionWeatherLane_TypeRoad_MarkingsSpot_ConditionsSide_WalkRoadJunctionCollision_TypeBAccident_RoadLandmark_firstlandmark_secondDistance_LandMark_FirstDistance_LandMark_SecondAccident_DescriptionLatitudeLongitude
301751YadgirYadgiri Traffic PS10978215920230042202312Road AccidentsCross roadsCity/TownNear office complexOtherHuman ErrorYesGrievous InjuryVehicle to VehicleNot at JunctionOthersNHBitumen(Tar)DryNot ApplicableClearNaNNaNNaNNaNNaNHit and RunNEAR GRREN CITY YADAGIRI02 KMNaN02 KMNaNಪೂರ್ವಕ್ಕೆ-ಯಾದಗಿರಿ ನಗರದ ಹೊಸ ನಿವೇಶನಗಳು ಹಾಕಿದ ಜಾಗ ರುತ್ತದೆ. ಪಶ್ಚಿಮಕ್ಕೆ-.ಯಾದಗಿರಿ ನಗರದ ಗ್ರೀನ್ ಸಿಟಿ ಹಾಗೂ ಸೆಂಚುರಿಯನ್ ಶಾಲೆಗೆ ರಸ್ತೆ ಇರುತ್ತದೆ ಉತ್ತರಕ್ಕೆ –ವಾಡಿ ಕಡೆ ಹೋಗುವ ಎನ್ ಹೆಚ್ 150 ರಸ್ತೆ ಇರುತ್ತದೆ. ದಕ್ಷಿಣಕ್ಕೆ – ಯಾದಗಿರಿ ನಗರ ಕಡೆ ಬರುವ ಎನ್ ಹೆಚ್ 150 ರಸ್ತೆ ಇರುತ್ತದೆ16.75840277.123453
301752YadgirYadgiri Traffic PS10978215920230049202311Road AccidentsNot ApplicableRural AreasOpen areaStraight and flatHuman ErrorYesFatalHit pedestrianNot ApplicableStraight and flatNHConcreteDryNot ApplicableFineNaNNaNNaNNaNNaNHit and RunYadgir-Hyderabad RoadNear Sakryanayak ThandaNaN100 mtNaNYadgir-Hyderabad Road, Near Sakryanayak Thanda16.77627977.207245
301753YadgirYadgiri Traffic PS10978215920230057202311Road AccidentsNot ApplicableCity/TownNarrow bridge or culvertsCurveHuman ErrorNoGrievous InjuryHit pedestrianNot ApplicableCurveState HighwayBitumen(Tar)DryNot ApplicableFineNaNNaNNaNNaNNaNHit parked vehicleNEAR BHEEMA BRIDGE YADAGIRI06 KM SOUT FROM PSNaN06 KMNaN.16.74180677.124326
301754YadgirYadgiri Traffic PS10978215920230059202312Road AccidentsCross roadsCity/TownNear office complexCurveHuman ErrorNoGrievous InjuryVehicle to VehicleNot at JunctionCurveMajor District RoadBitumen(Tar)DryNot ApplicableFineNaNNaNNaNNaNNaNHit parked vehicleNEAR MUNCIPALTY OFFICE YADAGIRI500 MNaN500 MNaNಪೂರ್ವಕ್ಕೆ- ಯಾದಗಿರಿ ನಗರದ ಸ್ವಪ್ನಾ ಟಾಕಿಜ್ ಇರುತ್ತದೆ. ಪಶ್ಚಿಮಕ್ಕೆ- ಯಾದಗಿರಿ ನಗರದ ನಗರ ಸಭೆ ಇರುತ್ತದೆ. ಉತ್ತರಕ್ಕೆ – ಯಾದಗಿರಿ ನಗರದ ಹತ್ತಿಕುಣಿ ಕಡೆಗೆ ಹೋಗುವ ಮುಖ್ಯ ರಸ್ತೆ ಇರುತ್ತದೆ. ದಕ್ಷಿಣಕ್ಕೆ – ಯಾದಗಿರಿ ನಗರದ ಕನಕ ವೃತ್ತದ ಕಡೆಗೆ ಹೋಗುವ ಮುಖ್ಯ ರಸ್ತೆ ಇರುತ್ತದೆ.16.76580477.132050
301755YadgirYadgiri Traffic PS10978215920230070202312Not ApplicableNot ApplicableCity/TownOpen areaOtherHuman ErrorNoGrievous InjuryVehicle to VehicleNot at JunctionOthersOthersBitumen(Tar)DryNot ApplicableFineNaNNaNNaNNaNNaNOthersNEAR BHIMAPPA TELAGAR FIELD YADAGRI03 KMNaN03 KMNaN.16.47700477.108417
301756YadgirYadgiri Traffic PS10978215920230010202312Road AccidentsCross roadsCity/TownNear School or CollegeOtherHuman ErrorNot ApplicableGrievous InjuryVehicle to VehicleNot at JunctionOthersNHBitumen(Tar)DryNot ApplicableClearNaNNaNNaNNaNNaNRun Off RoadNEAR DEGREE COLLEGE CROSS YADAGIRI01 KMNaN01 KMNaNಪೂರ್ವಕ್ಕೆ- ಯಾದಗಿರಿ ನಗರದ ಕಡೆ ಬರುವ ಎನ್ ಹೆಚ್ 150 ರಸ್ತೆ ಇರುತ್ತದೆ. ಪಶ್ಚಿಮಕ್ಕೆ- ವಾಡಿ ಕಡೆ ಹೋಗುವ ಎನ್ ಹೆಚ್ 150 ರಸ್ತೆ ಇರುತ್ತದೆ. ಉತ್ತರಕ್ಕೆ –.ಯಾದಗಿರಿ ಗ್ರಾಮೀಣ ಪೊಲೀಸ್ ಠಾಣೆ ಕಡೆಗೆ ಹೋಗುವ ರಸ್ತೆ ಇರುತ್ತದೆ. ದಕ್ಷಿಣಕ್ಕೆ – ಯಾದಗಿರಿ ಡಿಗ್ರಿ ಕಾಲೇಜ್ ಇರುತ್ತದೆ.16.75291477.127311
301757YadgirYadgiri Traffic PS10978215920230030202312Road AccidentsCross roadsCity/TownNear office complexCurveHuman ErrorNoGrievous InjuryVehicle to VehicleNot ApplicableCurveMinor District RoadBitumen(Tar)DryNot ApplicableFineNaNNaNNaNNaNNaNHit and RunNEAR TAMILUNADU BANK2 KMNaN1.5KMNaNಪೂರ್ವಕ್ಕೆ- ದೋಖಾ ಶಾಲೆ ಕಡೆ ಹೋಗುವ ರಸ್ತೆ ಇರುತ್ತದೆ. ಪಶ್ಚಿಮಕ್ಕೆ- ತಮುಳು ನಾಡು ಮತ್ತು ಯಶ್ ಬ್ಯಾಂಕ ಇರುತ್ತದೆ. ಉತ್ತರಕ್ಕೆ – ವಾಡಿ ಕಡೆಗೆ ಹೋಗುವ ಎನ್ ಹೆಚ್ 150 ಮುಖ್ಯ ರಸ್ತೆ ಇರುತ್ತದೆ. ದಕ್ಷಿಣಕ್ಕೆ –ಯಾದಗಿರಿ ನಗರದ ಸುಭಾಷ ವೃತ್ತ ಕಡೆಗೆ ಬರುವ ಮುಖ್ಯ ರಸ್ತೆ ಇರುತ್ತದೆ.0.0000000.000000
301758YadgirYadgiri Traffic PS10978215920230037202311Road AccidentsNot ApplicableCity/TownNear a factory industrial areaStraight and flatHuman ErrorNoFatalNot ApplicableNot ApplicableStraight and flatNHBitumen(Tar)DryNot ApplicableFineDualLaneNaNNaNNaNNaNSkidding or Self accidentYadgir-Hyderabad Road, Near Saidapur Hotel YadgirNear Saidapur Hotel YadgirNaN50 mtNaNYadgir-Hyderabad Road, Near Saidapur Hotel Yadgir0.0000000.000000
301759YadgirYadgiri Traffic PS10978215920230044202311Road AccidentsCross roadsCity/TownResidential areaCurveHuman ErrorNoGrievous InjuryHit pedestrianUncontrolledCurveNHBitumen(Tar)DryNot ApplicableClearNaNNaNNaNNaNNaNRun Off RoadNEAR GUNJ 2nd GATE YADAGIRI03 KMNaN03 KMNaNಪೂರ್ವಕ್ಕೆ-ಶ್ರೀ ಬಸವೇಶ್ವರ ಎ.ಪಿ.ಎಮ್.ಸಿ (ಗಂಜ್) ಎರಡನೇ ಗೇಟ್ ಇರುತ್ತದೆ. ಪಶ್ಚಿಮಕ್ಕೆ-.ಮುಖ್ಯ ರಸ್ತೆ ಅದರಾಚೆ ಸುಲೆಮಾನ್ ಸ್ಟೀಲ್ ಮತ್ತು ವೆಲ್ಡಿಂಗ್ ಶಾಪ್ ಇರುತ್ತದೆ ಉತ್ತರಕ್ಕೆ –ಹೈದ್ರಾಬಾದ ಮತ್ತು ಯಾದಗಿರಿ ಗಂಜ್ ಕ್ರಾಸ್ ಕಡೆಗೆ ಹೋಗುವ ಮುಖ್ಯ ರಸ್ತೆ ಇರುತ್ತದೆ. ದಕ್ಷಿಣಕ್ಕೆ – ಯಾದಗಿರಿ ಹೊಸಳ್ಳಿ ಕ್ರಾಸ್ ಕಡೆಗೆ ಬರುವ ಮುಖ್ಯ ರಸ್ತೆ ಇರುತ್ತದೆ16.76368277.147579
301760YadgirYadgiri Traffic PS10978215920230017202311Road AccidentsNot ApplicableCity/TownResidential areaOtherHuman ErrorNoGrievous InjuryHit pedestrianNot ApplicableNot ApplicableNHBitumen(Tar)DryNot ApplicableFineNaNNaNNaNNaNNaNHit parked vehicleIMPIRIYALGARDEN YADAGIRI03 KMNaN03 KMNaNಪೂರ್ವಕ್ಕೆ- ಹೈದ್ರಾಬಾದ ಕಡೆ ಹೋಗುವ ಎನ್.ಹೆಚ್.150 ರಸ್ತೆ ಇರುತ್ತದೆ. ಪಶ್ಚಿಮಕ್ಕೆ- ಯಾದಗಿರಿ ನಗರದ ಕಡೆ ಬರುವ ಎನ್ ಹೆಚ್ 150 ರಸ್ತೆ ಇರುತ್ತದೆ. ಉತ್ತರಕ್ಕೆ – ಖುಲ್ಲಾ ಜಾಗೆ ಗಂಜ್ ಇಂಡಸ್ಟಿಯಲ್ ಏರಿಯಾ ಇರುತ್ತದೆ. ದಕ್ಷಿಣಕ್ಕೆ – ಇಂಪಿರಿಯಲ್ ಗಾರ್ಡನ್ ಫಂಕ್ಷನ ಹಾಲ್ ಇರುತ್ತದೆ0.0000000.000000